




'''simple docstring'''







from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class 	_snake_case:
 __snake_case:							torch.Tensor  # [batch_size x 3]
 __snake_case:							torch.Tensor  # [batch_size x 3]
 __snake_case:							torch.Tensor  # [batch_size x 3]
 __snake_case:							torch.Tensor  # [batch_size x 3]
 __snake_case:							int
 __snake_case:							int
 __snake_case:							float
 __snake_case:							float
 __snake_case:							Tuple[int]






 def 						_UpperCamelCase (self       :  Any    )       ->   Any:






    """simple docstring"""


    assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
    assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
    assert len(self.x.shape    ) == len(self.y.shape    ) == len(self.z.shape    ) == len(self.origin.shape    ) == 2






 def 						_UpperCamelCase (self       :  int    )       ->   Dict:






    """simple docstring"""


    return torch.from_numpy(np.array([self.width, self.height]    ,		dtype=np.floataa    )    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   Tuple:






    """simple docstring"""


    return torch.from_numpy(np.array([self.x_fov, self.y_fov]    ,		dtype=np.floataa    )    )






 def 						_UpperCamelCase (self       :  int    )       ->   torch.Tensor:






    """simple docstring"""


    A__       =     torch.arange(self.height * self.width    )
    A__       =     torch.stack(
        [
            pixel_indices % self.width,
            torch.div(a    ,		self.width    ,		rounding_mode='trunc'    ),
        ]    ,		axis=1    ,		)
    return coords






 @property
 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   int:






    """simple docstring"""


    A__  ,			*A__       =     self.shape
    A__       =     int(np.prod(a    )    )

    A__       =     self.get_image_coords()
    A__       =     torch.broadcast_to(coords.unsqueeze(0    )    ,		[batch_size * inner_batch_size, *coords.shape]    )
    A__       =     self.get_camera_rays(a    )

    A__       =     rays.view(a    ,		inner_batch_size * self.height * self.width    ,		2    ,		3    )

    return rays






 def 						_UpperCamelCase (self       :  List[str]    ,		a       :  torch.Tensor    )       ->   torch.Tensor:






    """simple docstring"""


    A__  ,			*A__  ,			A__       =     coords.shape
    assert n_coords == 2
    assert batch_size == self.origin.shape[0]

    A__       =     coords.view(a    ,		-1    ,		2    )

    A__       =     self.resolution()
    A__       =     self.fov()

    A__       =     (flat.float() / (res - 1)) * 2 - 1
    A__       =     fracs * torch.tan(fov / 2    )

    A__       =     fracs.view(a    ,		-1    ,		2    )
    A__       =     (
        self.z.view(a    ,		1    ,		3    )
        + self.x.view(a    ,		1    ,		3    ) * fracs[:, :, :1]
        + self.y.view(a    ,		1    ,		3    ) * fracs[:, :, 1:]
    )
    A__       =     directions / directions.norm(dim=-1    ,		keepdim=a    )
    A__       =     torch.stack(
        [
            torch.broadcast_to(self.origin.view(a    ,		1    ,		3    )    ,		[batch_size, directions.shape[1], 3]    ),
            directions,
        ]    ,		dim=2    ,		)
    return rays.view(a    ,		*a    ,		2    ,		3    )






 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  int    ,		a       :  int    )       ->   "DifferentiableProjectiveCamera":






    """simple docstring"""


    assert width * self.height == height * self.width, "The aspect ratio should not change."
    return DifferentiableProjectiveCamera(
        origin=self.origin    ,		x=self.x    ,		y=self.y    ,		z=self.z    ,		width=a    ,		height=a    ,		x_fov=self.x_fov    ,		y_fov=self.y_fov    ,		)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []
   A__       =     []
   A__       =     []
   A__       =     []
   for theta in np.linspace(0      ,2 * np.pi      ,num=20						):
      A__       =     np.array([np.sin(UpperCAmelCase						), np.cos(UpperCAmelCase						), -0.5]						)
      z /= np.sqrt(np.sum(z**2						)						)
      A__       =     -z * 4
      A__       =     np.array([np.cos(UpperCAmelCase						), -np.sin(UpperCAmelCase						), 0.0]						)
      A__       =     np.cross(UpperCAmelCase      ,UpperCAmelCase						)
      origins.append(UpperCAmelCase						)
      xs.append(UpperCAmelCase						)
      ys.append(UpperCAmelCase						)
      zs.append(UpperCAmelCase						)
   return DifferentiableProjectiveCamera(
       origin=torch.from_numpy(np.stack(UpperCAmelCase      ,axis=0						)						).float()      ,x=torch.from_numpy(np.stack(UpperCAmelCase      ,axis=0						)						).float()      ,y=torch.from_numpy(np.stack(UpperCAmelCase      ,axis=0						)						).float()      ,z=torch.from_numpy(np.stack(UpperCAmelCase      ,axis=0						)						).float()      ,width=UpperCAmelCase      ,height=UpperCAmelCase      ,x_fov=0.7      ,y_fov=0.7      ,shape=(1, len(UpperCAmelCase						))      ,)








'''simple docstring'''







import argparse
import copy







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {}

   with open(UpperCAmelCase						) as f:
      for line in f:
         if line.split()[0] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[1], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[0]].append(
                [line.split()[1], line.split()[2]]						)
         if line.split()[1] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[0], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[1]].append(
                [line.split()[0], line.split()[2]]						)

   return dict_of_neighbours







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase						) as f:
      A__       =     f.read(1						)
   A__       =     start_node

   A__       =     []

   A__       =     start_node

   A__       =     0
   while visiting not in first_solution:
      A__       =     10000
      for k in dict_of_neighbours[visiting]:
         if int(k[1]						) < int(UpperCAmelCase						) and k[0] not in first_solution:
            A__       =     k[1]
            A__       =     k[0]

      first_solution.append(UpperCAmelCase						)
      A__       =     distance_of_first_solution + int(UpperCAmelCase						)
      A__       =     best_node

   first_solution.append(UpperCAmelCase						)

   A__       =     0
   for k in dict_of_neighbours[first_solution[-2]]:
      if k[0] == start_node:
         break
      position += 1

   A__       =     (
       distance_of_first_solution
       + int(dict_of_neighbours[first_solution[-2]][position][1]						)
       - 10000
   )
   return first_solution, distance_of_first_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []

   for n in solution[1:-1]:
      A__       =     solution.index(UpperCAmelCase						)
      for kn in solution[1:-1]:
         A__       =     solution.index(UpperCAmelCase						)
         if n == kn:
            continue

         A__       =     copy.deepcopy(UpperCAmelCase						)
         A__       =     kn
         A__       =     n

         A__       =     0

         for k in _tmp[:-1]:
            A__       =     _tmp[_tmp.index(UpperCAmelCase						) + 1]
            for i in dict_of_neighbours[k]:
               if i[0] == next_node:
                  A__       =     distance + int(i[1]						)
         _tmp.append(UpperCAmelCase						)

         if _tmp not in neighborhood_of_solution:
            neighborhood_of_solution.append(_tmp						)

   A__       =     len(neighborhood_of_solution[0]						) - 1

   neighborhood_of_solution.sort(key=lambda UpperCAmelCase						: x[index_of_last_item_in_the_list]						)
   return neighborhood_of_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     1
   A__       =     first_solution
   A__       =     []
   A__       =     distance_of_first_solution
   A__       =     solution

   while count <= iters:
      A__       =     find_neighborhood(UpperCAmelCase      ,UpperCAmelCase						)
      A__       =     0
      A__       =     neighborhood[index_of_best_solution]
      A__       =     len(UpperCAmelCase						) - 1

      A__       =     False
      while not found:
         A__       =     0
         while i < len(UpperCAmelCase						):
            if best_solution[i] != solution[i]:
               A__       =     best_solution[i]
               A__       =     solution[i]
               break
            A__       =     i + 1

         if [first_exchange_node, second_exchange_node] not in tabu_list and [
             second_exchange_node,
             first_exchange_node,
         ] not in tabu_list:
            tabu_list.append([first_exchange_node, second_exchange_node]						)
            A__       =     True
            A__       =     best_solution[:-1]
            A__       =     neighborhood[index_of_best_solution][best_cost_index]
            if cost < best_cost:
               A__       =     cost
               A__       =     solution
         else:
            A__       =     index_of_best_solution + 1
            A__       =     neighborhood[index_of_best_solution]

      if len(UpperCAmelCase						) >= size:
         tabu_list.pop(0						)

      A__       =     count + 1

   return best_solution_ever, best_cost







def 						_A      ( UpperCAmelCase=None						):






   '''simple docstring'''







   A__       =     generate_neighbours(args.File						)

   A__  ,			A__       =     generate_first_solution(
       args.File      ,UpperCAmelCase						)

   A__  ,			A__       =     tabu_search(
       UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,args.Iterations      ,args.Size      ,)

   print(F"""Best solution: {best_sol}, with total distance: {best_cost}."""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser(description='''Tabu Search''')
    parser.add_argument(
        '''-f''',
        '''--File''',
        type=str,
        help='''Path to the file containing the data''',
        required=True,
    )
    parser.add_argument(
        '''-i''',
        '''--Iterations''',
        type=int,
        help='''How many iterations the algorithm should perform''',
        required=True,
    )
    parser.add_argument(
        '''-s''', '''--Size''', type=int, help='''Size of the tabu list''', required=True
    )

    # Pass the arguments to main method
    main(parser.parse_args())


